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Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach
- Webber, Quinn M.R., Vander Wal, Eric
- Animal behaviour 2019 v.149 pp. 77-87
- animal behavior, birds, mammals, social networks, students
- The increased popularity and accessibility of social network analysis has improved our ability to test hypotheses about complex animal social structures. To gain a deeper understanding of the use and application of animal social network analysis, we systematically surveyed the literature and extracted information on publication trends from articles using social network analysis. We synthesize trends in social network research over time and highlight variation in the use of different aspects of social network analysis. The use of social network analysis in empirical articles has increased over time. In the context of social network methods, we found that many studies did not use an association index to account for missing individuals or observations of individuals; that the number and type of social network metrics calculated in a given study varied substantially (median=2); and that focal observation was by far the most common method used to generate social networks, although the use of biologging devices increased over time. We also observed that most species studied using social networks are mammals (55%) or birds (23%), and that the majority are species of least concern (59%; International Union for the Conservation of Nature, IUCN, www.iucn.org). Based on our findings, we highlight four key recommendations for future studies: (1) the use of association indices is almost always necessary; (2) the a priori selection of specific network metrics and associated hypotheses increases transparency; (3) combination of focal observation with biologging devices could improve our understanding of remotely sensed behaviours; and (4) because most studies rarely study species of conservation concern, it may be practical to generate networks for similar species or populations, which could help inform management decisions. We highlight emerging trends in social network research that may be valuable for distinct groups of social network researchers: students new to social network analysis, experienced behavioural ecologists interested in using social network analysis and advanced social network users interested in trends of social network research. Our findings also shed light on past research and provide guidance for future studies using social network analysis.